Systems and methods for evolving content for computer games

ABSTRACT

Systems and methods of evolving content for computer games are disclosed. One such method comprises tracking popularity according to one or more users of at least a portion of a plurality of game content items available in a game world. The game world is associated with the game. The game world also comprises evolving an additional game content item based on the popularity of the tracked game content items. The usage of each tracked game content item determines a reproduction availability of the tracked game content item. The game world also comprises inserting the additional game content item into the game world.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/219,107, filed Jun. 22, 2009, which is hereby incorporated byreference.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to computer games, and morespecifically, to systems and methods of evolving content for computergames.

BACKGROUND

Video game players and developers are familiar with the concept of “gamecontent,” which are objects which players encounter, wield, and/orinteract with while playing a game. Some examples of game content arelevels, maps, terrain, models, textures, weapons, vehicles, objects,items, etc. Some game developers provide tools which enable players tocreate their own content, but these tools usually require significanteffort to master and specialized knowledge beyond that of most players.Other games include a mechanism for randomizing content (e.g., a randommap generator). But randomization is useful only if it is tightlyconstrained to avoid generating undesirable content, and provides nomeans to direct the new content toward the kind of content that playersprefer.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a computer game.

FIGS. 2A-C illustrate operation of the evolutionary content generatorfrom FIG. 1, according to some embodiments disclosed herein.

FIG. 3 illustrates the use of a fitness score by the evolutionarycontent generator from FIG. 1, according to some embodiments herein.

FIG. 4. illustrates evolution from items already present in the gameworld 110 from FIG. 1, according to some embodiments herein.

FIG. 5 illustrates evolution by spawning from a predefined content pool,according to some embodiments herein.

FIG. 6 is a hardware block diagram of one embodiment of a computerimplementing the game from FIG. 1, according to some embodimentsdisclosed herein.

DETAILED DESCRIPTION

Disclosed herein is an algorithm for evolving game content whichautomatically evolves novel game content based on player behavior, asthe game is played. Specifically, new content evolves based on playerinteraction with existing content. Application of this algorithm allowsfor games that generate their own content to satisfy players, which hasthe potential to significantly reduce the cost of content creation andto increase the replay value of games.

FIG. 1 is a block diagram of a computer game 100, which may beimplemented on a general-purpose computer, a specialized game console,or any other computing device (e.g., mobile phone, personal digitalassistant, media player, etc.). Game 100 includes game logic 130, gameworld 110, and evolutionary content generator 120. Game world 110includes the set of content items (e.g., levels, terrain, maps, models,textures, weapons, vehicles, etc.) which are available to interact withplayers during the game. In the example of FIG. 1, game world 110includes two types of content items—weapons and vehicles—and threeinstances of each. Evolutionary content generator 120 generates newcontent items and inserts the new content into game world 110. Gamelogic 130 receives player inputs and processes these inputs to driveplayer interaction with game world 110, resulting in game play.

FIGS. 2A-C illustrate the evolution of content by evolutionary contentgenerator 120, according to some embodiments disclosed herein. Duringthe game, players 210 interact with content items 220 that exist inworld 110. This interaction results in a content distribution 230 amongplayers 210. Thus, at any point in time, world 110 includes distributedcontent items 230 as well as content items 240 that are available fordistribution to players 210 through their interaction with items 220.Some non-limiting examples of how an item 220 is distributed to a player210 include: a player picks up the object; a player buys the object; theplayer takes the object from another player; and the player is awardedthe object for some action within the game. As play progresses, players210 explore the game world 110 and discover new content that isgenerated by evolutionary content generator 120 and placed into theworld 110. As shown in FIG. 2B, the content distribution 230 changes asplay progresses because some items are more popular than others: players210 tend to keep content items with which they are satisfied, whilediscarding content items with which they are not satisfied in favor ofnewly discovered content. The interaction of users with content itemsaffects the evolution of newly generated content contained within theundistributed content 240 as shown in FIG. 2C. Specifically, contentitems that players 210 are satisfied with become parents of newgenerations of content, while content that is widely disliked filtersout of the game. As game play continues, additional generations areevolved. In this way, players 210 continually explore a succession ofchanging content in world 110.

Thus, one embodiment of evolutionary content generator 120 can bedescribed as: tracking popularity according to one or more users of atleast a portion of a plurality of game content items available in a gameworld, where the game world is associated with the computer game;evolving an additional game content item based on the popularity of thetracked game content items, where the usage of each tracked game contentitem determines a reproduction availability of the tracked game contentitem; and inserting the additional game content item into the gameworld.

Another embodiment of evolutionary content generator 120 can bedescribed as: tracking usage by one or more users of at least a portionof a plurality of game content items available in a game world, wherethe game world is associated with the computer game; selecting forreproduction a subset of the tracked game content items based on therespective usage, where the probability of selecting each of the trackedgame content items for reproduction is proportional to the usage;reproducing using the selected subset of game content items to generatean additional game content item; and inserting the additional gamecontent item into the game world; and inserting the additional gamecontent items into the game world.

In some embodiments, a player 210 is allowed to collect differentcontent items, and/or multiple instances of the same content items. Thatis, a player 210 may, at a particular point in game play, hold threeweapons and one vehicle. In some of these embodiments, the total numberof different items and/or total number of instances of the same item maybe limited. That is, the player 210 may be limited to three offensiveweapons and two defense mechanisms. In some embodiments, a player 210 isprovided with information about the characteristics of a content itembefore that item is selected. In some embodiments, a player 210 is givena preview or demonstration of how the content item operates rather thansimply being given information about the characteristics.

Thus, evolutionary content generator 120 directs evolution based onimplicit information expressed by user interaction with particular itemsof content. In some embodiments, user interaction is captured in afitness score for each item of content in game world 110.

FIG. 3 illustrates the use of a fitness score by evolutionary contentgenerator 120. In the example of FIG. 3, game world 110 includes threeitems of content (310; 320, 330) and generator 120 tracks acorresponding fitness score (340, 350, 360) for each. Each fitness score340, 350, 360 is computed based on player interaction with therespective item 310, 320, 330. For example, if game play involvesplayers choosing a weapon from a set of weapons available in world 110,then one embodiment of generator 120 gives a higher score to a weaponthat is chosen or used more often, or by more players, and gives a lowerscore to weapons that are not preferred by players. In anotherembodiment of generator 120, lower scores are associated with items thatare more preferred. Importantly, the fitness score is driven by normalplayer interaction with the game, and does not require specific playerfeedback in the form of a preference or grade for an item. Since scoresare tracked on an item basis, the content generated in a multi-playergame is not merely an average of all player preferences, but is insteadunique content reproduced from individual items that are popular.

For some types of content items, an initial fitness score is assignedwhen the user selects the item, and the score increases while the userkeeps the items (possibly up to a maximum value). For other types ofitems, an item's score increases as it is selected by more users. Forstill other types of items, an initial score is assigned and is thenincreased according to specific actions (e.g., score increases each timea weapon is fired)

Having explained how fitness is tracked, several different types ofevolution will now be described. One embodiment of evolutionary contentgenerator 120 breeds using items already in game world 110 to producenew generations of content. The content items that are chosen to beparents are selected based on fitness of the items. In some embodiments,the parents are chosen probabilistically, such that the chance of beingchosen as a parent is proportional to the item's fitness.

An example of evolution from items already present in game world 110 isillustrated in FIG. 4. In this example, game world 110 includes items410, 420, 430, and 440. Evolutionary content generator 120 selects items410, 420, 440 for breeding based on fitness scores. As indicated byarrow 450, item 410 breeds with item 420 to produce a next generationitem 460. As indicated by arrow 470, item 420 also breeds with item 440to produce next generation item 480. The fitness score of item 430 doesnot qualify that item for breeding. Mutation and crossover may occurduring reproduction, so that children may become more complex than theirparents.

As can be seen in FIG. 4, after an initial round of breeding, game world110 contains content items from the original generation (i.e., 410, 420,440) as well as content items from a new generation (i.e., 460, 480).The example shown in FIG. 4 uses “sexual” reproduction (i.e., multipleparents, though not limited to two), but other embodiments use “asexual”reproduction (i.e., only one parent), while still others usedcombinations of the two. All forms of reproduction involve some form ofmutation or crossover of characteristics, which distinguishesreproduction from cloning.

Thus, the process used by the embodiment of content generator 120 shownin FIGS. 3 and 4 includes: assigning fitness scores to multiple items ofgame content based on player usage of the respective items; selecting aset of the items, based on the fitness scores; and adding new items ofgame content into the game world by spawning using the selected items asparents.

Another embodiment of evolutionary content generator 120 introduces newcontent into game world 110 by cloning from a separate predefinedcontent pool rather than being reproduced from parents that alreadyreside in the world. An example of evolution by cloning is illustratedin FIG. 5. In this example, the items residing in game world 110initially include items 510, 520, 530, and 540, while a predefined pool550 (outside of world 110) includes items 560, 570, and 580.Evolutionary content generator 120 then evolves the content by cloningitem 560 to produce item 560-1 and places item 560-1 into the world 110.Generator 120 also clones item 580-1 and places items 580-1 into theworld. In some embodiments, the selection of items for cloning israndom.

In some embodiments, designers of the game initially select items forpredefined pool 550. Some embodiments of evolutionary content generator120 receive content descriptions from game designers and create thepredefined pool 550 from these descriptions.

In some embodiments, the items residing in predefined pool 550 are itemswith desirable characteristics and are thus expected to be preferred byusers. For example, if the items are weapons, then weapons in thepredefined pool have characteristics like high accuracy, long range,high stopping power, etc. As another example, if the items are vehicles,then vehicles in the predefined pool have characteristics like highmaximum speed, high acceleration, good cornering, etc.

Cloning from predefined pool 550 ensures that diversity is not lost andthat good types of content from the past (i.e., those that users liked)might reappear. Additionally, cloning ensures an initial seed of goodcontent when the game first starts and players' preferences are unknown.

In some games, players 210 are provided content items with which tostart play. Some embodiments of evolutionary content generator 120provide players 210 with randomized content at the start. Otherembodiments of evolutionary content generator 120 start the game bydistributing items to players 210 from a starter pool of content items.Because all players begin the game with it, content in the starter poolcontent does not contribute to evolution. Therefore, content in thestarter pool is not actually used as a parent in the reproductionprocess. However, the fitness of starter pool content items does changeas users interact with these items. Therefore, it is possible for astarter pool item to be selected during evolution for reproduction. Insuch cases, an item is randomly selected instead from predefined pool550, and this item from predefined pool 550 is spawned into the gameworld.

Some embodiments of evolutionary content generator 120 utilize arecently-used pool which includes content items that were recentlypossessed by players 220 who subsequently left game world 110. Thisrecently-used pool queue provides a reasonable number of content itemswhich can be used as parents for breeding even when a small number ofplayers is participating in the game.

Some embodiments of evolutionary content generator 120 utilize anarchive pool which includes all content that achieves a particular levelof fitness during game play. When such content reaches this level offitness (which varies among types of games), it is automatically savedto the archive pool.

Some embodiments of evolutionary content generator 120 combinesreproduction from parents residing in game world 110 with cloning frompredefined pool 550. In some of these embodiments, the probability ofnew content being introduced into game world 110 through reproduction isP_(R) and the probability of new content being introduced frompredefined pool 550 is P_(NE). These values are typically set by thegame designer.

Creation and insertion of new content items into game world 110 may betriggered by various user actions. For example, destruction of an enemy,or a group of enemies, or enemies at a particular location may triggerinsertion of a new weapon. Triggers for creation and insertion of newcontent items into game world 110 may be related to time, e.g., mayoccur at predefined intervals, at random times, etc. Triggers forcreation and insertion of new content items into game world 110 may berelated to a number of items, e.g., to maintain a predefined number ofitems not yet distributed to players 210, to maintain a predefinednumber of total items in world 110, to maintain a predefined number ofitems of each type, etc.

In some embodiments, a new content item is spawned into the game worldby one of multiple methods, with the particular method chosen beingbased on a fixed probability. Two of the methods were discussed above:reproduction from the current content pool existing in the game world,with random selection of parent(s); and cloning from predefined pool550. A third method is random generation using a variety of itemcharacteristics (e.g., weapon range, weapon projectile size, weaponprojectile rate, etc.)

One example of the probabilities used to select these methods of itemgeneration are as follows: random=10%; predefined pool=10%;reproduction=80%. if a starter item (i.e., from the starter pool) isselected for reproduction, then an item from predefined pool 550 iscloned instead. Higher fitness items have a higher chance of producingoffspring, thereby enabling players to directly affect the course ofevolution.

According to the content generation methods disclosed herein, thepopulation size (i.e. those items that are eligible at any given time toreproduce) is variable and depends entirely on the number of users inthe system. That is, when more users play the game, more items receive ascore and are thus eligible for being chosen as parents.

Content items generated according to the methods disclosed herein arenot immediately eligible to reproduce. Instead, these items are in aspecial temporary state (i.e., placed somewhere in game world), in whichthe item may join the population only if a user chooses to acquire it orinteract with it.

According to the content generation methods disclosed herein, thedetermination as to which items leave the population (i.e., are nolonger eligible for reproduction) is made by users, by simply discardingitems, rather than by fitness scores.

According to the content generation methods disclosed herein, users donot explicitly communicate to the system which content they like.Instead, the preferred content is deduced by the system implicitly fromnatural human behavior. That is, users do not need to know that they areinteracting with an evolutionary algorithm yet evolution still worksanyway.

According to the content generation methods disclosed herein, the stepsof the method are asynchronous. That is, players may cause content tojoin the population or be eliminated at any time.

In some embodiments, each game content item is represented by anartificial neural network (ANN) and reproduction as discussed in FIG. 4is performed according to the NeuroEvolution of Augmenting Topologies(NEAT) algorithm. NEAT begins evolution with a population of small,simple networks and complexities the network topology into diversespecies over generations, leading to increasingly sophisticatedbehavior. The NEAT algorithm is further described in “Evolving neuralnetworks through augmenting topologies”, K. O. Stanley and R.Mikkulainen, Evolutionary Computation, vol. 10, pp. 99-127, 2002, theentirety of which is incorporated herein by reference.

To keep track of which gene is which while new genes are added, NEATuniquely assigns a historical marking to each new structural component.During crossover, NEAT aligns genes with the same historical markings,efficiently producing meaningful offspring. Traditionally, speciation inNEAT protects new structural innovations by reducing competition betweendiffering structures and network complexities. However, in theapplications described herein, the usual speciation procedure in NEAT isunnecessary because a human performs selection rather than an automatedprocess.

The process of complexification, which resembles how genes are addedover the course of natural evolution, allows NEAT to establishhigh-level features early in evolution and then later elaborate on them.For evolving content, complexification means that content can becomemore elaborate and intricate over generations.

In some embodiments, each game content item is represented by acompositional pattern-producing network (CPPN). CPPNs are a variation ofartificial neural networks (ANNs) that differ in their set of activationfunctions and how they are applied. While ANNs often contain onlysigmoid or Gaussian activation functions, CPPNs can include both suchfunctions and many others. The choice of CPPN functions can be biasedtoward specific patterns or regularities. For example, periodicfunctions such as sine produce segmented patterns with repetitions,while symmetric functions such as Gaussian produce symmetric patterns.Linear functions can be employed to produce patterns with straightlines. In this way, CPPN-based systems can be biased toward desiredtypes of patterns by carefully selecting the set of available activationfunctions.

Having described general principles, application of those principles toan example will now be described: the Galactic Arms Race (GAR) game. Thegoal of GAR is to pilot a space ship to defeat enemies, gain experience,earn money, and most importantly, to find advantageous new weapons thatare automatically generated by the evolutionary algorithm describedearlier. That is, in GAR the content items which undergo evolution areweapons.

GAR has a single-player mode, in which evolution is directed by theactions of a single player battling aliens. The aliens are controlled byscripted steering behaviors rather than players.

In one embodiment described herein, each player weapon is a particlesystem. Particle systems are usually defined by (1) a set of points inspace and (2) a set of rules guiding their behavior and appearance, e.g.velocity, color, size, shape, transparency, rotation, etc. However, suchrule sets can be complex and are usually hand-coded. In GAR, the playerweapon contains a single evolved CPPN. For every frame of animation,each particle issued from the weapon inputs its current positionrelative to the ship (px, pz) and distance from the ship (dc) into theCPPN. There are two, rather than three, spatial inputs because the gameis entirely situated on the y=0 plane. The CPPN is activated and outputsthe particle's velocity (vx, vz) and color (r, g, b) for that animationframe.

Weapon fitness in GAR is automatically calculated based on player usagestatistics, gathered as follows. Players possess up to N weapons at onetime in an arsenal. When a player fires a weapon, that weapon (which isa unique member of the population) gains fitness at a constant rate andthe other weapons in that player's arsenal lose fitness at the samerate. In some embodiments, the minimum fitness is 1 and there is nomaximum value. This sort of fitness decay mechanism for unused weaponsemphasizes emerging new weapon trends.

In GAR, one trigger even for spawning a new weapon is destruction of anenemy base or an enemy commander. As described earlier, methods forspawning a new weapon include reproduction within the current populationpresent in game world 110 and cloning from predefined pool 550, with aprobabilistic method based on weapon fitness randomly deciding whichweapon(s) reproduces.

In single-player GAR, the weapon population includes only the weaponsthe single player currently holds in his arsenal. In multi-player GAR,the weapon population includes the weapons currently held by allplayers. Thus single-player evolution is to some extent greedy; however,it is not equivalent to a normal evolutionary algorithm with apopulation of N because the player encounters a significant number ofweapon previews in addition to the weapons in the ship's currentarsenal. Therefore, the player is in effect judging such previews bytaking them or not.

When the game begins, players have no history of weapon preference. Onepossible policy is to initially give players N random weapons. However,such randomization could cause new players to receive N undesirableweapons. A better solution is for players to begin the game with apredefined set of starter weapons. The starter weapons are viable: shootonly in a straight line, and not eligible to reproduce during evolution.Thus, new players are guaranteed to begin with viable weapons.

Because starter weapons cannot reproduce and players begin the game withonly starter weapons, a method is needed to start evolution. For thispurpose, predefined pool 550 includes a diverse collection of acceptableweapons generated (in some cases, evolved) by the game developers. Ifthe weapon selected for reproduction by the evolutionary algorithm is astarter weapon (e.g., selected because the player fires that starterweapon often), the evolutionary process clones a random weapon frompredefined pool 550 into game world 110.

One advantage of predefined pool 550 is that it provides a jump start toevolution at the beginning of the game. Thus, developers can influencewhat weapons players will see early in the game. Predefined pool 550 canalso serve as a hall of fame, to which popular weapons are retired,possibly reappearing later in the game.

In one embodiment of GAR, the content items are particle system weapons,and each player weapon contains a single evolved CPPN. In every frame ofanimation during game play, each particle issued from the weapon inputsits current position relative to the ship (px, pz) and distance from theship (dc) into the CPPN. Two spatial inputs are used when the game istwo-dimensional, i.e., entirely situated on the y=0 plane. The CPPN isactivated and outputs the particle's velocity (vx, vz) and color (r, g,b) for that animation frame. Representing particle velocity and color inthis manner produces a wide of variety of vivid patterns. Because CPPNsare a superset of ANNs, which can approximate any function, particleweapons in GAR can theoretically evolve any conceivable pattern.

Calculation of fitness based on player usage may account for differentinfluences. As one example, certain weapons, by their nature, requiremore shots to be effective (e.g. wall guns for blocking incomingprojectiles). As another example, players that participate in the gamemore often might disproportionately influence evolution (i.e. by firingtheir weapons more often). To address these two influences, someembodiments of the evolutionary processed disclosed herein calculatefitness as follows. When a player fires a weapon, that weapon (which isa unique member of the population) gains fitness at a constant rate andthe other weapons in that player's arsenal lose fitness at the samerate. This fitness decay mechanism for unused weapons emphasizesemerging new weapon trends and ensures that weapons that require morefiring do not come to dominate. Furthermore, the minimum fitness is 1and the maximum fitness is 1,000, which means that older players do notcreate a disproportionate effect.

FIG. 6 is a hardware block diagram of one embodiment of a computer 600implementing the game 100 of FIG. 1. The computer contains a number ofcomponents that are familiar to a person of ordinary skill in the art,including a processor 610, memory 620, non-volatile storage 630 (e.g.,hard disk, flash RAM, flash ROM, EEPROM, etc.), a display 640, and oneor more input devices 650. The components are coupled via one or morebuses 660. The input devices can include, but are not limited to:keyboard, mouse, touch pad, touch screen, motion-sensitive input device,gesture-sensitive input device, inertial input device, gyroscopic inputdevice, joystick, game controller, etc. Omitted from FIG. 6 are a numberof conventional components, known to those skilled in the art, that arenot necessary to explain the operation of the computer.

The game 100 can be implemented by specialized hardware logic, software(i.e., instructions executing on a processor), or a combination thereof.Hardware embodiments include (but are not limited to) a programmablelogic device (PLD), programmable gate array (PGA), field programmablegate array (FPGA), an application-specific integrated circuit (ASIC), asystem on chip (SoC), and a system in package (SiP). In a softwareembodiment, memory 620 stores various software components which areexecuted by processor 610, for example, an operating system 620 and game100.

These executable components can be embodied in any computer-readablemedium for use by any processor which fetches and executes instructions.In the context of this disclosure, a “computer-readable medium” can beany means that can contain or store the program for use by, or inconnection with, the processor. The computer readable medium can bebased on electronic, magnetic, optical, electromagnetic, orsemiconductor technology.

Specific examples of a computer-readable medium using electronictechnology would include (but are not limited to) the following: arandom access memory (RAM); a read-only memory (ROM); an erasableprogrammable read-only memory (EPROM or Flash memory). A specificexample using magnetic technology includes (but is not limited to) aportable computer diskette. Specific examples using optical technologyinclude (but are not limited to) a portable compact disk read-onlymemory (CD-ROM) and a digital video disc read-only memory (DVD-ROM).

The foregoing description has been presented for purposes ofillustration and description. It is not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Obviousmodifications or variations are possible in light of the aboveteachings. The embodiments discussed, however, were chosen and describedto illustrate the principles of the invention and its practicalapplication to thereby enable one of ordinary skill in the art toutilize the invention in various embodiments and with variousmodifications as are suited to the particular use contemplated. All suchmodifications and variation are within the scope of the invention asdetermined by the appended claims when interpreted in accordance withthe breadth to which they are fairly and legally entitled.

We claim:
 1. A method for evolving one or more new game content items ina game world of a computer game, comprising: a) providing a plurality ofdifferent types of existing game content items in the game world; b)interacting by the first player with a first item of said plurality ofdifferent types of existing game content items in the game world, c)distributing, as a result of the interaction, the first item to thefirst player such that the first item becomes a distributed content itemwhich is available only to the first player for a portion of a timeperiod; wherein, of the plurality of different types of existing gamecontent items, the different types of existing game content items thatare not the distributed content item are an undistributed content item;wherein, at any point in time, there is the distributed content item andthe undistributed content item of the plurality of different types ofexisting game content items, and the undistributed content item isavailable to the plurality of players for the time period; furtherwherein the undistributed content item remains available in the gameworld for distribution to any of the plurality of players through theirinteraction with said undistributed content item; d) tracking theinteraction of the first player with said distributed content item andassigning a fitness score to the distributed content item based at leastin part on an amount of interaction between the first player and thedistributed content item; e) randomly selecting a selected probabilityamong a first probability, a second probability, and a thirdprobability; f) using the selected probability to create,probabilistically, a new content item using an evolutionary contentgenerator wherein the first probability is proportional to the fitnessscore of the distributed content item; wherein the second probability isa chance the new content item is randomly generated using one or moreitem characteristics; wherein the third probability is a chance the newcontent item is selected from a predefined pool; wherein the new contentitem is available, for the time period, to the plurality of players;wherein the new content item has one or more characteristics in commonwith the distributed content item; and (g) iteratively repeating steps(b)-(f) continuously.
 2. The method of claim 1, further comprising:cloning the new content item from the distributed content item when thefitness score of the distributed content item exceeds a popularitythreshold of that distributed content item; wherein the popularitythreshold of the distributed content item is a minimum fitness score. 3.The method of claim 1, further comprising using the distributed contentitem to generate the content item.
 4. A computer program productevolving a new game content item available to a player in a game worldof a computer game, the computer program comprising a computer readablestorage medium having program instructions embodied therewith, whereinthe computer readable storage medium is not a transitory signal per se,the program instructions are readable by a computer to cause thecomputer to perform a method comprising the steps of: providing aplurality of different types of existing game content items in the gameworld; interacting by the first player with a first item of saidplurality of different types of existing game content items in the gameworld, distributing, as a result of the interaction, the first item tothe first player such that the first item becomes a distributed contentitem which is available only to the first player for a portion of a timeperiod; wherein, of the plurality of different types of existing gamecontent items, the different types of existing game content items thatare not the distributed content item are an undistributed content item;wherein, at any point in time, there is the distributed content item andthe undistributed content item of the plurality of different types ofexisting game content items, and the undistributed content item isavailable to the plurality of players for the time period; furtherwherein the undistributed content item remains available in the gameworld for distribution to any of the plurality of players through theirinteraction with said undistributed content item; tracking theinteraction of the first player with said distributed content item andassigning a fitness score to the distributed content item based at leastin part on an amount of interaction between the first player and thedistributed content item; randomly selecting a selected probabilityamong a first probability, a second probability, and a thirdprobability; using the selected probability to create,probabilistically, a new content item via an evolutionary contentgenerator; wherein the first probability is proportional to therespective fitness score of the distributed content item; wherein thesecond probability is a chance the new content item is randomlygenerated using one or more item characteristics; wherein the thirdprobability is a chance the new content item is selected from apredefined pool; further wherein in a first scenario, the new contentitem is created based on the highest probability of the first, second,and third probabilities, and upon creation, the new content item is oneof the undistributed content item.